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Deep learning shows good reliability for automatic segmentation and volume measurement of brain hemorrhage, intraventricular extension, and peripheral edema

  • Imaging Informatics and Artificial Intelligence
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objectives

To evaluate for the first time the performance of a deep learning method based on no-new-Net for fully automated segmentation and volumetric measurements of intracerebral hemorrhage (ICH), intraventricular extension of intracerebral hemorrhage (IVH), and perihematomal edema (PHE) in primary ICH on CT.

Methods

Three hundred and eighty primary ICH patients who underwent CT at hospital arrival were divided into a training cohort (n = 300) and a validation cohort (n = 80). An independent cohort with 80 patients was used for testing. Ground truth (segmentation masks) was manually generated by radiologists. Model performance on lesion segmentation and volumetric measurement of ICH, IVH, and PHE were evaluated by comparing the model results with the segmentations performed by radiologists.

Results

In the test cohort, the Dice scores of lesion segmentation were 0.92, 0.79, and 0.71 for ICH, IVH, and PHE, respectively. The sensitivities were 0.93 for ICH, 0.88 for IVH, and 0.81 for PHE. The positive predictive values were 0.92, 0.76, and 0.69 for ICH, IVH, and PHE, respectively. Excellent concordance (concordance correlation coefficients [CCCs] ≥ 0.98) of ICH and IVH and good concordance of PHE (CCCs ≥ 0.92) were demonstrated between manually and automatically measured volumes. The model took approximately 15 s to provide automatic segmentation and volume analysis for each patient.

Conclusion

Our model demonstrates good reliability for automatic segmentation and volume measurement of ICH, IVH, and PHE in primary ICH, which can be useful to reduce the effort and time of doctors to calculate volumes of ICH, IVH, and PHE.

Key Points

• Deep learning algorithms can provide automatic and reliable assessment of intracerebral hemorrhage, intraventricular hemorrhage, and perihematomal edema on CT.

• Non-contrast CT-based deep learning method can be helpful to provide efficient and accurate measurements of ICH, IVH, and PHE in primary ICH patients, thereby reducing the effort and time of doctors to segment and calculate volumes of ICH, IVH, and PHE in primary ICH patients.

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Abbreviations

CCCs:

Concordance correlation coefficients

GCS:

Glasgow Coma Scale

ICH:

Intracerebral hemorrhage

IVH:

Intraventricular extension of intracerebral hemorrhage

PHE:

Perihematomal edema

PPV:

Positive predictive value

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Funding

This study has received funding from Shanghai Municipal Health Commission (grant number 2019SY061).

Author information

Authors and Affiliations

  1. Shanghai Institute of Medical Imaging, Shanghai, China

    Xianjing Zhao & Zhenwei Yao

  2. Department of Radiology, Huashan Hospital, Fudan University, 12 Middle Urumqi Road, Jing’an District, Shanghai, 200040, China

    Xianjing Zhao, Shiman Wu & Zhenwei Yao

  3. Ping An Technology (Shenzhen) Co., Ltd., Shanghai, China

    Kaixing Chen, Ge Wu, Guyue Zhang, Xin Zhou, Chuanfeng Lv & Guotong Xie

  4. Department of Radiology, Xiangyang Central Hospital, Hubei University of Arts and Science, Xiangyang, China

    Yun Chen

  5. Ping An Health Cloud Company Limited, Shanghai, China

    Guotong Xie

  6. Ping An International Smart City Technology Co., Ltd., Shanghai, China

    Guotong Xie

Authors
  1. Xianjing Zhao
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Correspondence to Zhenwei Yao.

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The scientific guarantor of this publication is Zhenwei Yao.

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The authors of this manuscript declare relationships with the following company: Ping An Technology (Shenzhen) Co., Ltd.

Statistics and biometry

One of the authors has significant statistical expertise.

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Written informed consent was waived by the Institutional Review Board.

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• retrospective

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• performed at one institution

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Zhao, X., Chen, K., Wu, G. et al. Deep learning shows good reliability for automatic segmentation and volume measurement of brain hemorrhage, intraventricular extension, and peripheral edema. Eur Radiol 31, 5012–5020 (2021). https://doi.org/10.1007/s00330-020-07558-2

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  • DOI: https://doi.org/10.1007/s00330-020-07558-2

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